Encoding for computation: recognizing brief dynamical patterns by exploiting effects of weak rhythms on action-potential timing.

Abstract

Many stimuli have meaning only as patterns over time. Most auditory and many visual stimuli are of this nature and can be described as multidimensional, time-dependent vectors. A simple neuron can encode a single component of the vector in a firing rate. The addition of a small subthreshold oscillatory current perturbs the action-potential timing, encoding the signal also in a timing relationship, with little effect on the coexisting firing rate representation. When the subthreshold signal is common to a group of neurons, the timing-based information is significant to neurons receiving inputs from the group. This information encoding allows simple implementation of computations not readily done with rate coding. These ideas are examined by using speech to provide a realistic input signal to a biologically inspired model network of spiking neurons. The output neurons of the two-layer system are shown to specifically encode short linguistic elements of speech.